NG-Aqua: Coarse-Grained Near-Global Aqua-Planet Simulation with Dynamical Tendencies

Coarse-grained 3D state data from near-global cloud-resolving model simulations known as NG-Aqua. The dataset supplements the original simulation with computed dynamical tendencies for total water and liquid-ice potential temperature. Noah Brenowitz from the University of Washington posted this dataset, which is derived from simulations run using the System for Atmospheric Modeling (SAM).

Use Cases

• Train machine learning models for subgrid-scale parameterization based on the coarse-grained 3D state fields.
• Analyze dynamical tendencies (FQT and FSLI) for total water and liquid-ice potential temperature.
• Validate atmospheric model physics using data from high-resolution (4km) cloud-resolving model simulations.
• Benchmark data assimilation or forecasting algorithms with near-global aqua-planet simulation outputs.

Strengths

• Simulation data is derived from a high-resolution 4km cloud-resolving model.
• Includes computed dynamical tendencies (FQT and FSLI) not present in the original dataset.
• Data is provided in the netCDF format, a standard for scientific data.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and total dataset size are unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

University of Washington, Noah Brenowitz

Collection Method

Generated from near-global cloud-resolving model (CRM) simulations using the System for Atmospheric Modeling (SAM). Dynamical tendencies were computed by initializing a lower-resolution SAM run with coarse-grained fields and evolving the state.

Geography

Near-global coverage (aqua-planet simulation).